System for organizing and fast searching of massive amounts of data

ABSTRACT

A system to collect and analyze performance metric data recorded in time-series measurements, converted into unicode, and arranged into a special data structure. The performance metric data is collected by one or more probes running on machines about which data is being collected. The performance metric data is also organized into a special data structure. The data structure at the server where analysis is done has a directory for every day of performance metric data collected with a subdirectory for every resource type. Each subdirectory contain text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.

the virtual private network sends its data. If the monitored server runssoftware which generates rich graphics, the bandwidth requirements goup. This can be a problem and expensive especially where the monitoredserver is overseas in a data center in, for example, India or China, andthe monitoring computer is in the U.S. or elsewhere far away from theserver being monitored.

Another method of monitoring a remote server's performance is to put anagent program on it which gathers performance data and forward thegathered data to the remote monitoring server. This method also suffersfrom the need for a high bandwidth data link between the monitored andmonitoring servers. This high bandwidth requirement means that thenumber of remote servers that can be supported and monitored is asmaller number. Scalability is also an issue.

Other non IT systems generate large amount of data that needs to begathered, organized, stored and searched in order to evaluate variousissues. For example, a bridge may have thousands of stress and strainsensors attached to it which are generating stress and strain readingsconstantly. Evaluation of these readings by engineers is important tomanaging safety issues and in designing new bridges or retrofittingexisting bridges.

Once performance data has been gathered, if there is a huge volume ofit, analyzing it for patterns is a problem. Prior art systems such asperformance tools and event log tools use relational databases (tablesto store data that is matched by common characteristics found in thedataset) to store the gathered data. These are data warehousingtechniques. SQL queries are used to search the tables of time-seriesperformance data in the relational database.

Several limitations result from using relational databases and SQLqueries. First, there is a ripple that affects all the other roews ofexisting data as new indexes are computed. Another disadvantage is theamount of storage that is required to store performance metric datagathered by the minute regarding multiple attributes of one or moreservers or other resources. Storing performance data in a relationaldatabase engenders an overhead cost not only in time but also money inboth storing it and storing it in an indexed way so that it can besearched since large commercial databases can be required if the amountof data to be stored is large.

Furthermore, SQL queries are efficient when joining rows across tablesusing key columns from the tables. But SQL queries are not good when theneed is to check for patterns in values of columns in a series ofadjacent rows. This requires custom programming in the form of “storedprocedures” which extract the desired information programmatically. Thisis burdensome, time consuming and expensive to have to write a customprogram each time a search for a pattern is needed. As the pattern beingsearched for becomes more complex, the complexity of the storedprocedure program also becomes more complex.

The other way of searching for a pattern requires joining the table withitself M−1 number of times and using a complex join clause. This becomesimpractical as the number of joins exceeds 2 or 3.

As noted earlier, the problems compound as the amount of performancedata becomes large. This can happen when, for example, receivingperformance data every minute from a high number of sensors or from alarge number of agents monitoring different performance characteristicsof numerous monitored servers. The dataset can also become very largewhen, for example, there is a need to store several years of data. Largeamounts of data require expensive, complex, powerful commercialdatabases such as Oracle.

There is at least one prior art method for doing analysis of performancemetric data that does not use databases. It is popularized by thetechnology called Hadoop. In this prior art method, the data is storedin file systems and manipulated. The primary goal of Hadoop basedalgorithms is to partition the data set so that the data values can beprocessed independent of each other potentially on different machinesthereby bring scalability to the approach. Hadoop technique referencesare ambiguous about the actual processes that are used to process thedata.

Therefore, a need has arisen for an apparatus and method to reduce theamount of performance data that is gathered so that more sensors orservers can be remotely monitored with a data link of a given bandwidth.There is also a need to organize and store the data without using arelational database and to be able to search the data for patternswithout having to write stored procedure programs, or do table joins andwrite complex join clauses.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a typical server on which the processesdescribed herein for organizing, storing and searching performance datacan run.

FIG. 2 is an example of a directory structure storing one day'sperformance data on a resource the performance of which is beingmonitored remotely.

FIG. 3 is another example of a file system containing a separatedirectory for storing performance metric data for three different daysfor three different resources, each resource having two groups ofattributes.

FIG. 4 is a diagram of the directory structure of an example of datacollected by a probe.

FIG. 5 is a flowchart of the high level process the monitoring serverperforms to receive probe data and stored it in the directory structurefor search and analysis.

FIG. 6 is a template for a regular expression used to explain the syntaxof a typical regular expression query.

FIG. 7 is a flowchart of one embodiment of the Query Request Handlermodule.

FIG. 8, comprised of FIGS. 8A through 8C, is a flowchart of theprocessing of the probe data importer.

FIG. 9, comprised of FIGS. 9A and 9B, is a diagram of the modules in thesystem and a flowchart of the processing of the NRDB Access managermodule.

FIG. 10 is a block diagram of one embodiment of the overall systemincluding the major functional modules in the central server calledMegha, where the query request processing for analysis of performancemetric data occurs and where the NDRB stores the performance metric dataand configuration data.

FIG. 11 is a flowchart of the processing by one embodiment of the QueryRequest Processor.

DETAILED DESCRIPTION OF THE VARIOUS EMBODIMENTS

There is disclosed herein apparatus and processes for infrastructureperformance data analysis (and analysis of other large amounts ofperformance data) which uses search techniques instead of relationaldatabases to store and organize data. Data is stored in a special folderand directory structure with one directory for every day's worth ofdata. This allows data to be collected, processed and stored at a fasterrate. Performance data is stored in a file system having one directoryfor each day. All the performance data collected from one or moreresources in an IT environment or one or more sensors in some otherenvironment on the day corresponding to the directory is stored in fileswithin the directory. There is a subdirectory for each resource wherethe directory name is the signature for that resource. There is one filefor a group of attributes. Each attribute file has N sections, one foreach attribute defined to be in the group. Each section has M values,where M values comprise the entire times series of values for thatattribute for the entire day corresponding to the resource.

The result is that all the collected performance data is stored aspatterns; the patterns being data from many sources which are sorted andstored in a time series in the special directory structure describedabove; so all data from all sources for a particular day is stored inone directory structure. This data structure allows the data set to besearched with time as one axis and each data element as the other axis.

Attribute values are stored either as band values or delta values. Eachvalue for an attribute for a particular reading on a particular day isstored as Java UTF-8 encoded string with each value encoded a singleUnicode character. In other words, the numbers of each performancemetric value are converted to letters of a Java UTF-8 encoded string.This allows searching using standard regular expressions the syntax ofwhich is known and comprises a form of formal language. The variouselements of syntax can be used to construct search queries which searchthrough the performance data for patterns. Regular expressions can onlysearch text and not numbers and that is why the performance metricreadings or values have their numbers converted to text before storage.

The syntax of regular expression is rich with tools that allow complexsearches and pattern analysis simply by writing an expression of theproper syntax thereby eliminating the time consuming need to write acustom program or “stored procedure” in SQL to do the same thing insearching the data of a relational database.

Unicode is a computing industry standard for the consistent encoding,representation and handling of text expressed in most of the world'swriting systems. It is a set of approximately 1 million characters thatspan from hex 0 to hex 10FFFF. There are enough unicode characters todevote a single one to every symbol in the Japanese and Chineselanguages and all the alphabets in the world and all the numbers inwhich performance metrics are expressed. Each performance metric valuereceived from an agent is converted to one of these unicode characters.

Searching the performance data with regular expressions definingparticular patterns of data from certain resources which satisfy certainconditions expressed in the regular expressions is analogous tosearching large amounts of text for keywords and reporting only thoseportions of the text which fit a certain semantic usage.

The performance metric data is automatically converted by the system toUnicode strings of alphabetic characters from the set of 109,000characters in the Unicode Standard.

The use of regular expressions allows complex patterns of performancedata to be searched without having to write complex, custom programscalled “stored procedures” which would be necessary if a relationaldatabase was used to store the data and SQL was used to search thedatabase.

The system of the invention allows users to draft their search queriesas regular expressions. The user must know the syntax of regularexpressions in order to do this unless the user wishes to only usepredefined searches which some embodiments of the system of theinvention provide for selection and execution by a user. A regularexpression provides a concise and flexible means for matching strings oftext, such as particular characters, words, or patterns of characters.

A regular expression is written in a formal language that can beinterpreted by a regular expression processor, a program that eitherserves as a parser generator or examines text and identifies parts thatmatch the provided specification.

Storing the Unicode characters encoding the performance metric data inthe special directory structure described herein eliminates the need foruse of an expensive database system such as Oracle even where very largeamounts of data are collected and stored.

The performance data is collected by agent programs which are coupled tothe sensors or are programmed on the IT resources being monitored. Theseagent programs collect, compress and send the performance data over thedata link to the remote monitoring server which collects it, converts itto Unicode and stores it in the directory structure defined above. Theremote monitoring server also provides an interface for a user tocompose regular expression search queries and also provided “canned”searches which can be run by a user, each canned search being apredefined regular expression which the user may modify slightly to suithis or her purposes.

The process and apparatus for collecting, storing and processingperformance metric data differs from SQL Database technology in at leasttwo ways. First, the partition algorithm stored performance data basedupon time slices. Data is stored in file systems sorted by time slices.A time slice represents a point in time and over time, and there aremany such slices. Unlike a traditional database, this technique allowsthe inventors to not impact the overall database when new data for atime slice is introduced or a new time slice is created. That is, thereis no ripple effect.

Storing the data in time slices in the special directory structure,examples of which are shown in FIGS. 2 and 3, allows the data to besearched with time as one axis and each data element as the other axis.This is analogous to searching a large amount of text for keywords andthen reporting only those portions of text that fit a certain semanticusage.

The second difference is that the method of analysis and search of theperformance data is based upon regular expressions which are used tosearch Unicode encoded text where the performance metric numbers havebeen converted to Unicode text characters. Regular expressions have afixed, predefined syntax and semantics (together considered a grammar)and a variety of expressions can be formed using this syntax andsemantics to search the performance data for patterns that meet criteriaexpressed in the regular expressions composed for the custom search.Regular expressions can be derived for all different kinds of search tolimit the search to particular resources, particular attributes of thoseresources, particular days or particular time intervals duringparticular days, etc. Great flexibility is provided without thecomplexity and labor of having to write custom programs in the form ofstored procedures to find the right data and analyze it.

The processes described here to search and analyze performance metricdata are inspired by and somewhat similar to XPATH technology. XPATH isa technique used to traverse XML document data. XPATH-like techniquesare used here to analyze infrastructure performance metric data andchanges to that data over time. The processes described herein extendthe XPATH notions to the search and analysis of data organized andstored by time slice which makes the search and analysis techniquestaught herein efficient and fast. Search and analysis of the performancedata is done using path-based techniques. A graph is created thatrepresents the data. The graph G is a representation of vertex and edges(V,E). An edge connects two vertices and vertex has the ability toevaluate an expression and then, based on the expression, allow for atraversal through an appropriate edge.

FIG. 1 is a block diagram of a typical server on which the processesdescribed herein for organizing, storing and searching performance datacan run. Computer system 100 includes a bus 102 or other communicationmechanism for communicating information, and a processor 104 coupledwith bus 102 for processing information. Computer system 100 alsoincludes a main memory 106, such as a random access memory (RAM) orother dynamic storage device, coupled to bus 102 for storing informationand instructions to be executed by processor 104. Main memory 106 alsomay be used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by processor104. Computer system 100 further usually includes a read only memory(ROM) 108 or other static storage device coupled to bus 102 for storingstatic information and instructions for processor 104. A storage device110, such as a magnetic disk or optical disk, is provided and coupled tobus 102 for storing information and instructions. Usually theperformance data is stored in special directory structures on storagedevice 110.

Computer system 100 may be coupled via bus 102 to a display 112, such asa cathode ray tube (CRT) of flat screen, for displaying information to acomputer user who is analyzing the performance data. An input device114, including alphanumeric and other keys, is coupled to bus 102 forcommunicating information and command selections to processor 104.Another type of user input device is cursor control 116, such as amouse, a trackball, a touchpad or cursor direction keys forcommunicating direction information and command selections to processor104 and for controlling cursor movement on display 112. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane.

The processes described herein to organize, store and search performancedata uses computer system 100 as its hardware platform, but othercomputer configurations may also be used such as distributed processing.According to one embodiment, the process to receive, organize, store andsearch performance data is provided by computer system 100 in responseto processor 104 executing one or more sequences of one or moreinstructions contained in main memory 106. Such instructions may be readinto main memory 106 from another computer-readable medium, such asstorage device 110. Execution of the sequences of instructions containedin main memory 106 causes processor 104 to perform the process stepsdescribed herein. One or more processors in a multi-processingarrangement may also be employed to execute the sequences ofinstructions contained in main memory 106. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions to implement the invention. Thus, embodiments ofthe invention are not limited to any specific combination of hardwarecircuitry and software.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to processor 104 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media include, for example, optical or magnetic disks, suchas storage device 110.

Volatile media include dynamic memory, such as main memory 106.Transmission media include coaxial cables, copper wire and fiber optics,including the wires that comprise bus 102. Transmission media can alsotake the form of acoustic or light waves, such as those generated duringradio frequency (RF) and infrared (IR) data communications. Common formsof computer-readable media include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, DVD, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, a PROM, and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of computer readable media may be involved in supplyingone or more sequences of one or more instructions to processor 104 forexecution. For example, the instructions may initially be borne on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 100 canreceive the data on a telephone line or broadband link and use aninfrared transmitter to convert the data to an infrared signal. Aninfrared detector coupled to bus 102 can receive the data carried in theinfrared signal and place the data on bus 102. Bus 102 carries the datato main memory 106, from which processor 104 retrieves and executes theinstructions. The instructions received by main memory 106 mayoptionally be stored on storage device 110 either before or afterexecution by processor 104.

Computer system 100 also includes a communication interface 118 coupledto bus 102. Communication interface 118 provides a two-way datacommunication coupling to a network link 120 that is connected to alocal network 122. For example, communication interface 118 may be anintegrated services digital network (ISDN) card or a modem to provide adata communication connection to a corresponding type of broadbank linkto the internet. As another example, communication interface 118 may bea local area network (LAN) card to provide a data communicationconnection to a compatible LAN. Wireless links may also be implemented.In any such implementation, communication interface 118 sends andreceives electrical, electromagnetic or optical signals that carrydigital data streams representing various types of information.

Network link 120 typically provides data communication through one ormore networks to other data devices. For example, network link 120 mayprovide a connection through local network 122 to a host computer 124 orto data equipment operated by an Internet Service Provider (ISP) 126.ISP 126 in turn provides data communication services through theworldwide packet data communication network, now commonly referred to asthe “Internet” 128. Local network 122 and Internet 128 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 120 and through communication interface 118, which carrythe digital data to and from computer system 100, are exemplary forms ofcarrier waves transporting the information.

Computer system 100 can send messages and receive data, includingprogram code, through the network(s), network link 120, andcommunication interface 118. In the Internet example, a server 130 whichis having its performance data monitored might transmit performance datavia an agent program that collects it through Internet 128, ISP 126,local network 122 and communication interface 118 to computer system100. The received performance data is stored and can be searched by theprocesses described later herein.

The system according to the teachings of the invention has on thesoftware and data side the following components which are executed andstored on the hardware platform described above or similar.

Data Store Manager; Query Request Handler; Data Access Manager; ProbeInterface; and

Proprietary non-relational database referred to as the NDRB and detailedin the Directory Structure heading below and illustrated in FIGS. 2 and3

Data Store Manager

This component receives data from probes in a well defined format, itand stores it in NRDB. A probe is an external software program whichcollects data on a periodic basis from an external data source andwrites data into a format which can be processed by Data Store Manager.The Data Store Manager can have any program structure so long as it canreceive data in the probe data format described elsewhere herein,decompress it and store it in the NDRB in the directory structure anddata format described herein for the NDRB. In the preferred embodiment,it will have a program structure which can perform the processing of theflowchart of FIG. 5. It can run on any off the shelf computer havingsufficient speed, memory capacity and disk capacity to store theperformance data being collected.

Query Request Handler

This component accepts search queries from external applications orusers, and provides back the results. The query language is aproprietary syntax for regular expressions which is given below underthe Query Definition Language Heading, and which provides constructs forspecifying search patterns to analyze data. The Query Request Handlercan have any program structure which can receive query requests withregular expressions embedded therein having the syntax described below,and parse those queries and perform the processing of the flowchart ofFIG. 7. It can run on any off the shelf computer having sufficientspeed, memory capacity and disk capacity to store the performance databeing collected.

Data Access Manager

This component provides access to the data stored in Megha's proprietarynon-relational database (NRDB). This internal employs standard cachingtechniques to provide results faster. The Data Access Manager can haveany program structure which can access directory structures like thoseof the NDRB of which FIGS. 3 and 4 are examples, and which supports theQuery Request Handler requests for data from the NDRB to perform theprocessing of the flowchart of FIG. 7. It can run on any off the shelfcomputer having sufficient speed, memory capacity and disk capacity tostore the performance data being collected.

Probe Interface NRDB

All the data in Megha is stored in NRDB. NRDB uses a normal file systemconsisting of files and folders. It uses a special folder structure andspecial encoding of data files to optimize the storage and access ofdata.

The entire software that implements the Data Store Manager, the SearchHandler, the Data Access Manager and the Probe Interface, in thepreferred embodiment is designed to run on commodity hardware inside aJava virtual machine. Commodity hardware is defined as regularlyavailable Intel x86/64 architecture based computers. Standard Linuxdistribution such as CentOS is used as the base operating system.

As an example of how the system works to collect performance metric dataand analyze it, suppose server 130 is a server which has a couple ofvirtual machines running on it the performance of which is to bemonitored. The performance metric data for each virtual machine iscollected by an agent or probe process (not shown) or, in someembodiments, a separate probe process for every virtual machine. Theperformance data is gathered on a per day basis to measure variousperformance metrics on server 130. Performance data of the server 130itself such as CPU cycle utilization, hard disk access time, hard diskcapacity, etc. may also be gathered. There are usually several metricsthat are measured simultaneously, often on a per minute basis.

This performance metric data gathered by the agent process is compressedand packetized and the packets are sent over the internet 128 to ISP 126to which a local area network 122 is connected. The local area networkis coupled via a network line 120 to the communications interface 118 ofthe monitoring server system 100.

Probe Data Format

The performance metric data for every element is collected by a probe. Aprobe is a program running on the computer having the element orattribute being monitored. The probe for each element periodically orsporadically (usually a call is made every minute) makes applicationprogrammatic interface calls to the operating system of the computer orother machine to gather the performance data on the element it ismonitoring. The probes can be any agent hardware and/or softwarecombination that can collect the desired performance metric data and putit into the data format described below for probe data.

Probes don't have to be just for IT attributes. They can also gatherdata for mechnical structures or automative systems. For example,engineers designing bridges may attach temperature and strain sensors atvarious positions on the structures, each of which is read by a probeprogram running on a computer which periodically interrogates eachsensor from time to time, takes its reading and sends it elsewhere forstorage and analysis. The probe gathers all the sensor data, formats thedata into the data structure format described below, compresses the datastructure and packetizes the compressed data for transmission over anydata path to a system elsewhere for analysis. Likewise for cars,engines, etc. The probe system is more or less like the modern dayequivalent of telemetry systems used on satellites and missiles thatfeed performance data back to an earth station by a radio telemetrylink.

The performance metric data values gathered by the probes are typicallypacketized for transmission over the internet. The primary objective ofthe probe data format is to reduce the amount of data which probe willproduce so as to reduce bandwidth requirements on the data link overwhich the probe data is sent. This reduces the amount of storagerequired to store the data and also makes the transmission to anotherlocation faster. The probe programs do not do the conversion of theperformance metric data to unicode in the preferred embodiment, but insome alternative embodiments, they could.

The probe collects all the attribute data for one day on all theelements it is monitoring and creates a directory structure such as theone shown in FIG. 4. The directory structure contains files which storethe time series of attribute values (performance metric data) for everyattribute for which the probe collected data. The attribute values arenumbers and are not converted by the probe to unicode values. Thathappens at the monitoring server end.

In FIG. 4, block 180 represents the top level directory, block 182represents a folder for all host type elements, block 184 represents afolder for all disk type elements being monitored. Each of the folders182 and 184 contains a text file that contain the attribute valuesobtained by the probe for every element being monitored of the typesymbolized by the subdirectory. Each text file that contains all theperformance metric values for all the monitored elements in the samegroup with one row containing the performance metric values measured forone of the elements being monitored in that group. For example, the hostfolder 182 may have a single text tile A1.txt, but that file containsmultiple row, one for each host element being monitored. For example,blocks 186 and 188 contain the performance metric values for twoparticular hosts being monitored in the group within A1.txt called H1and H2. H1 and H2 in blocks 186 and 188 represent unique strings whichuniquely identify the hosts for which the performance metric data wascollected. H1 has 1440 performance metric measurements stored in the rowsymbolized by the V1, V2 . . . V1440 values in a comma delimited list.for host H1, a performance value was measured every minute. Same forhost H2. Blocks 190 and 192 contain performance metric values collectedby the probe for two disks D1 and D2 in the group of monitored elements“disk” represented by folder 184. These performance metric values fordisks D1 and D2 are stored in different sections or rows of a text filenamed A2.txt.

The whole collection of data files and subdirectories is zipped by theprobe into one zip file which is a compressed version of the datastructure. By sending a compressed version of the data, the bandwidthrequirement on the data path between the probe and the monitoringserver(s) is greatly reduced. When the zip file is unzipped, the datastructure like that in FIG. 4 (or whatever the data structure is thenumber of elements and attributes being monitored) results.

Any payload produced by the probe must conform to the followingstructure:

The first file namedListOfFiles<YYYYMMDD_HHmmSS>_<base64 encoded text of encrypted value of(SiteName+“_”+ServerName+“_”+ArraySerialNumber)>_<ProbeType>.txt

-   -   Each line inside this file will have name of a file which is        part of this payload        -   If the file has configuration or events data, the file must            be named Conf<YYYYMMDD_HHmmSS>_<base64 encoded text of            encrypted value of            (SiteName+“_”+ServerName+“_”+ArraySerialNumber)>_<ProbeType>.zip.enc        -   If the file has performance data, the file must be named            Perf<YYYYMMDD_HHmmSS>_<base64 encoded text of encrypted            value of            (SiteName+“_”+ServerName+“_”+ArraySerialNumber)>_<ProbeType>.zip.enc

Where:

-   -   SiteName—name of the site assigned for the probe    -   ServerName—name of the entity from which data is being        collected, it is the text filled in by the user during probe        configuration.    -   ArraySerialNumber—Optional additional information to further        identify the entity.    -   ProbeType—Type of entity from which data is being        collected—VMWare, SMIS, NetApp, Amazon ECS, Bridge Sensors

One or more .zip file as identified in the list of files

The configuration zip file contains one or more files which can be oftwo types:

Snapshot

Mini-snapshot

Snapshot

The snapshot type file contains the entire configuration about the datasource to which the probe is connected. The name of this file is: <SiteName>_<DataSource>_snapshot_<YYYYMMDD>_<HHMMSS>_<Version>.txt, where:

-   -   <Site Name>: Identifier for location (actual physical site)        where the probe is situated    -   <Data Source>: Identifier for the data source (resource, i.e.,        host, disk array, printer, etc.) from which the data is being        collected    -   <YYYYMMDD>_<HHMMSS>: The date and time when the snapshot was        made    -   <Version>: Version of the file.        The file format of snapshot is as follows:

%meta probe_id:<Identifier> probe_type:<Probe Type> probe_site:<SiteName> probe_server:<Server Name> probe_version:<Probe Version> %meta {t:<YYYMMDD_HHMMSS> { R:<ResourceType>#<Resource Id>O:{<ResourceType>#<Another_Resource_id>,}+? b: <Begin TimeYYYMMDD_HHMMSS >? e:<End Time YYYMMDD_HHMMSS >? a:{<AttributeId>=<Attribute Value>}+ r:{<Resource Type>#<Resource Id>,}+ $:{<EventId> <space><Event String>}+ }+ }+

Example

% metaprobe_id:Cust_(—)192.168.0.63probe_type:VMWareprobe_site:Cust1probe_server:192.168.0.63probe_version:10%% metat:20110624_(—)062248R:dc#Cust192.168.0.63_datacenter-2a:name=MTNVIEWR:ds#Cust_(—)192.168.0.63_datastore-205a:name=FAS960_homea:capacity=51322806272a:freeSpace=42685091840a:uncommitted=17323200512a:provisionedSpace=25960914944a:type=NFSa:URL=netfs:7/192.168.0.50//vol/vol0/home/a:sioc=disabledr:h#Cust1_(—)192.168.0.63_host-171,R:ds#:Cust1_(—)192.168.0.63_datastore-10a:name=Storage1$:AlarmSnmpCompleted Alarm ‘Host error’—an SNMP trap for entity192.168.0.48 was sent

Updates

As configuration changes and configuration related events occur, theywill be written to a mini snapshot file. The name of this file will be:<Site name>_<Data

Source>_minisnapshot_<YYYYMMDD_(>)_<HHMMSS_(>)_<version>.txt

<YYYYMMDD>_<HHMMSS>:

The format of this file is exactly same as the snapshot file. Theprimary difference is that it will have only have a subset of the dataof the snapshot type of file. The subset captures the changes which haveoccurred in configuration data since the last time a snapshot file wasmade.

Performance Data

The performance data is a zip file which must have the followingdirectory structure:

-   -   <YYMMDD_HHMMSS>—This directory name the start time of the time        series specified in this data set        -   <Resource Type>—One directory for each resource type            -   <Attribute Id>.txt—One file for each performance metric                Each <Attribute Id>.txt has one or more lines where each                line has the following format:

<Resource Signature>‘,’ {Value} ‘,’ {‘,’<Value>}+

The value list is a time ordered series of values for that performancemetric for the resource specified at the beginning of the time. If themetric value does not exist for a particular point in time, then a blankor empty value is allowed.

NRDB File System Structure

The performance metric data is stored in a filesystem structure asdefined below. One directory is created for each day in the formatYYYYMMDD. All performance data for all the resources in the data modelfor a particular day are stored in this directory. Under this directory,there is a directory for each resource where the directory name is thesignature of that resource. Under this directory, there is one file fora group of attributes. The directory will look something like this:

<YYYYMMDD> - One Folder for each day <Resource Type><AttributeGroupId>.perf

-   -   <YYMMDD_HHMMSS>—This directory name contains the start time of        the time series specified in this data set        -   <Resource Type>—One directory for each resource type            -   <Attribute Id>.txt—One file for each performance metric                <AttributeGroupld>.perf file stores processed values for                each sample in a compressed format. This format is now                described in detail.

The file is divided into “n” number of sections. Where “m” is theattributes which are defined to be in the same group. Each section willhold “m” number of values—the entire time series values of that day forthat resource's attribute. So, for example, if the probe samplinginterval is 1 minute then there will be 1440 (1440 minutes in a day)values. Each <Attribute Id>.txt has one or more lines where each linehas the following format:

<Resource Signature>‘,’ {Value} ‘,’ {‘,’<Value>}+

The value list is a time ordered series of values for that performancemetric for the resource specified at the beginning of the time. If themetric value does not exist for a particular point in time, then a blankor empty value is allowed.

Currently, corresponding to each raw value of a performance metricattribute received from the probe, two types of processed value arestored:

-   -   Band value        -   An attribute can define the “fidelity” with which it will            store the raw value. This is called in Band Factor. Band            factor is an integer with a minimum value of 1 and maximum            of any positive integer value. With a band factor of 1,            there is no loss of fidelity. The processed value is same as            raw value. With a band factor 10, the processed value will            be 1/10th of the raw value rounded to the nearest integer.    -   Delta value        -   It is the change in percentage from band value at time t−1            and band value at time t.

Each set of 1440 values of a performance metric attribute (assuming onevalue is measured every minute) are stored as a Java UTF-8 encodedString. Each performance metric attribute value is encoded as a singleUnicode character in the String.

FIG. 2 is an example of a directory structure storing one day'sperformance data on a resource the performance of which is beingmonitored remotely. The processor 104 in FIG. 1 is programmed byinstructions stored in main memory 106, according to one embodiment ofthe invention, to create a special directory structure with onedirectory for each day's worth of data, and one subdirectory for eachresource for which performance metric data is being received. In FIG. 2,block 150 represents the directory created for storing the performancemetric data collected on Aug. 14, 2011. The subdirectory represented byblock 152 represents the subdirectory where performance data for theresource E1 is to be stored. Suppose in this example, that resource E1is the server 130 in FIG. 1.

Each subdirectory has the directory name in its signature. In this case,subdirectory 152 has 20110814 in its directory name which is the name ofthe directory of which it is a part.

Each subdirectory contains one attribute file for each group ofattributes that are being measured by the performance metric data thatstores performance metric values. Each attribute file has N sections,one section for each attribute defined to be in the group for which thefile was created. Each section holds M performance metric values for theparticular attribute whose values are recorded in that section. Thatsection's data comprises the entire time series of values for theattribute to which the section is devoted.

In the example of FIG. 2, there are only two groups of attributes insubdirectory 152 so there are only two files 154 and 156. Suppose eachof these files represents one of the virtual machines running on server130. Each file is a time slice of performance metric data values thatrecords the entire day's worth of a metric in the section of that filedevoted to storing values for that performance metric. Typically, if ametric has a measured value every minute, the section of the filedevoted to that metric will have 140 comma delimited values for thatmetric encoded as a Java UTF-8 encoded string. UTF-8 is a multibytecharacter encoding for unicode. UTF-8 can represent every character inthe unicode character set. Each of the 1,112,064 code points in theunicode character set is encoded in a UTF-8 string comprised of one tofour 8-bit bytes termed octets. The earlier characters in the unicodecharacter set are encoded using fewer bytes leading to greaterefficiency. The first 128 unicode character set coincide with the 128ASCII characters.

The system of the invention has a mapping table that maps performancemetric values into unicode characters and then encodes them with UTF-8.Since unicode only supports positive values, the unicode range is splitand a first range of unicode values is mapped to positive performancemetric values and a second range of unicode values is mapped to negativeperformance metric values.

Each performance metric value from a measurement is encoded as a singleunicode character in the hexadecimal number system (hex).

Each new day's worth of data from all resources and all probes is storedin a new directory structure. The names of the directories,subdirectories and files include information about the day during whichthe data was gathered, the resources from which it was gathered and theparticular group of attributes whose performance metric data is storedin the various sections of the file.

In the example of FIG. 2, the directory structure 150 has files 154 and156 for one day of metric data gathered every minute for two differentmetrics from the same resource, represented by subdirectory 152. Inother words, there is only one resource being monitored. Also, for theexample of FIG. 2, there is only one attribute in each group ofattributes and only two attributes total have performance metric datagathered. The performance metric data is gathered on Aug. 14, 2011 sothe directory 150 created to store that day's metric data is named20110814. There is only one resource being monitored called E1 so thereis created a subdirectory 152 called 20110814_E1. That subdirectorycontains two files. The first file 154 is named E1/G1, and it stores themetric values for metric 1 in group 1 (which has only one sectionbecause there is only one metric M1 in the group E1/G1). The values ofmetric M1 are gathered every minute and are symbolized as values V1through V1440 which are stored as a comma delimited list. The value V1is the value of metric M1 taken at time 00:01:01 on Aug. 14, 2011, i.e.,the first minute of Aug. 14, 2011. The value V2 is the value of metricM1 taken at time 00:02:01 on Aug. 14, 2011, the second minute of Aug.14, 2011. The value V1440 is the value of metric M1 taken at time23:59:01 which is the last minute of Aug. 14, 2011. Therefore, theposition of any particular value on the comma delimited list denotes thetime at which the value was captured on Aug. 14, 2011.

The second file 156 in the resource E1 subdirectory is named E1/G2 andit stores values for a metric M2 in group 2 (which also only has onemetric in the group so there is only one section in the file). It hasnot been shown in detail since it has the same structure as the fileE1/G1.

The values stored in each position of the file are Unicode encodedmeaning the numeric value of the metric's value has been mapped to atext character or string of characters in the encoding process.

This allows these values to be searched using regular expressions whichare a form of formal language (used in the sense computer scientists usethe term “formal language”) which has predefined rules of syntax andsemantics (together called its grammar). The elements from which regularexpressions can be formed are known and each element has its own knownsyntax for how it is structure and has its own unique and knownsemantics defining what it means. Persons wishing to analyze theperformance metric data in any way, can compose a regular expressionusing the available elements for composing a regular expression andtheir syntax and semantics.

FIG. 3 is another example of a file system containing a separatedirectory for storing performance metric data for three different daysfor three different resources, each resource having two groups ofattributes. The file system storing metric data is represented by block158. Three days of performance data are stored in directories 160, 162and 164, respectively. Each of these directories has threesubdirectories named R1, R2 and R3, each of which is a folder whichcontains actual files of text data encoding performance metric valuesthat have been measured and transmitted by the agents. Blocks 166 and168 represent comma delimited text files named GRP1.TXT and GRP2.TXTstoring the performance metric data gathered on Jul. 27, 2011 forresource 1 for group 1 and group 2 attributes, respectively.

The reason for grouping different attributes performance values in thesame file is for speed of loading and analysis. Typically, an analysisof a resource will involve looking at patterns or values or valuechanges of several different attributes over a particular interval. Ifthe attributes involved in the analysis are all grouped in the samegroup, they will be stored in the same file. In this way, all the dataneeded to do the analysis can be loaded into memory for analysis simplyby reading appropriate file containing the attribute group for theresource under analysis from the directory structure corresponding tothe day of interest. That file is loaded into memory by a standard fileaccess call to the operating system, and the regular expression searchor searches can be performed on the data. This is faster than having toload several different files or having to do SQL queries to a databasewhich would require a larger number of reads.

FIG. 5 is a high level flowchart of the process the monitoring serverperforms to receive the zip files of performance metric data from aprobe, recover the data and store it. Block 200 represents the processof receiving the zip file of performance metric data from the probe.Block 202 represents the process of decompressing the zip file torecover the data structure such as that shown in FIG. 4. Block 204represents the process of converting the numerical performance metricvalues stored in the text files to unicode characters using a mappingtable the server uses for such purposes. Block 206 represents theprocess of storing the unicode data structure derived in step 204 in theappropriate parts of the NDRB data structure. Usually this just entailsstoring the entire directory and all its files on disk since the datastructure is already structured as one directory for the particular dayon which the data was collected with individual text files of metricdata for each element being monitored in subdirectories for the type ofelement each text file represents.

Example of how a Regular Expression can be Used to Analyze The MetricPerformance Data

Suppose an analyst wanted to know if CPU utilization was between 90% and100% for at least 5 minutes or more. The regular expression syntax tomake a search and analysis of the performance metric data for CPUutilization would be in generic syntax:

[U90−U100]{5,}-100 -200

To convert this regular syntax to take into account the unicode encodingof the CPU utilization metric values, suppose a CPU utilization metricvalue representing 90% utilization is mapped to unicode hex character a,92.5% CPU utilization is mapped to unicode hex character b, 95% to hexcharacter c, 97.5% to hex character d, and 100% to hex character e. IfCPU utilization metric values are measured every minute, then a regularexpression to determine if the CPU utilization was between 90% and 100%for at least 5 minutes would be:

[a-e]{5}[g]which means if five consecutive values in the file storing CPUutilization values for the CPU in question on the day in question wereany combination of hex characters a through e, then the expressionevaluates to true. This means that every time on that particular day theCPU utilization metric values had five consecutive values which were anycombination of hex a through hex e, then for each of those intervals,the CPU utilization was between 90% and 100%. This may mean the CPU ismaxing out and another CPU should be added.

The preferred embodiment of the invention, the user must know the syntaxof regular expressions in order to compose his or her query. Inalternative embodiments, a user interface is provided which allows theuser to think in the problem space and compose his queries in plainEnglish, and the system converts that query into the proper syntax for aregular expression which will perform that query and analysis. In someembodiments, the software portion of the system of the inventionpresents a user interface which has a set of predefined searches whichthe user can use to do various forms of analysis. Each predefinedsearch, when selected causes a regular expression to be generated andused to search the performance metric data and return the results. Insome embodiments, these predefined searches are templates which havevariables that can be set by the user. For example, there may be apredefined search to determine if CPU utilization is between x % and y %for more than z minutes where x, y and z are variables that the user canset before the search is run.

To run a search/query, in the preferred embodiment, the software of thesystem of the invention displays a query expression box and two timerange boxes, one for a start time and one for an end time. These startand end time boxes are calendars in the preferred embodiment, and theuser simply picks the first day for which data is to be examined andpicks a second day in the end time calendar which is the last day ofdata to be examined. He then types his query into the query expressionbox in the syntax of the regular expression and hit return. The softwarethen automatically accesses the appropriate directory structures for theday or days specified by the user, accesses the appropriate files thatcontain the performance metric attribute values as specified in thequery expression, reads those attribute values into memory and examinesthe data using the logic specified in the query expression.

FIG. 6 is a template for a regular expression used to explain the syntaxof a typical regular expression query. The h at the beginning of theregular expression indicates that this particular query is designed tosearch host performance metric data. If the query was about disks orsomething else, something indicative of the type of resource in questionwould be in the place of the h.

The large left bracket indicates the beginning of the actual queryexpression. The @ symbol at the beginning of the query expression is akeyword. The “CPU usage” term is the name of the attribute data to besearched and it is this attribute name which causes the software to lookup the correct file name which contains the performance metric data forCPU usage. The “rx” term indicates that what follows is a regularexpression, and the “b” term indicates that the type of search is forband data as opposed to delta data. The [U90-U100]{5} is a regularexpression that indicates the actual criteria to be used in performingthe band data search, i.e., it defines which performance metric datasatisfy the query and which do not. The regular expression could also bea pointer to another regular expression stored in a file. The pointerwould contain a unique ID for the regular expression to be used.

The band values are computed or mapped values for internalrepresentation of numbers which are greater than the highest numberwhich can be unicoded (around 1,000,000). For example, if a datatransfer rate is 20 million bits per second and the metric is20,000,000, a band value will be computed for that metric using areduction factor of, for example 10 million so as to reduce the 20million number to the number 2 before it is unicoded. Any reductionfactor that brings the range of a performance metric which is a highnumber down into the unicode range may be used for internalrepresentation purposes. The searches are then done on the computed bandvalues and not the actual performance metric numbers.

Delta values are useful for analyzing performance metric data thatspikes. A delta value records how much a value has changed since theprevious time it was measured.

The system, in the preferred embodiment, calculates and stores both aband value and a delta value for some or all performance metrics.

Query Definition Language Objectives

-   -   Be able to traverse from a set of resources to another set of        related resources and so on    -   At each stage of traversal apply certain filtering criteria:        -   Configuration attributes: Matching certain value, change in            value        -   Relations: Addition or deletion of a relation        -   Performance metrics: Matching certain patterns

Basic Syntax Building Blocks That May Be Used To Build A Query

XPath style data processing/filtering and this processing will beapplied to various search queries.

<Resource Type>/<*Related resource type>[=<conf attrld> rx <regex>ORIAND . . . ][˜<conf attr id> . . . ][@<perf attr id> <rx bld>|rxld<regex or regex pattern id>][$<event id , , , ][+|−<related resourcetype]/{Related resource type/ . . . } {Related resource type/ . . . }Relation traversal:<resource type>/<related resource type>/ . . .Ex: v/h/dThe above expression will result the following path:v->h->d

Multiple Traversal Paths:

<resource type>/{related resource type>/ . . . } {another related type>/. . . }Ex: v/{h/n}{r/d}The above expression results to the following traversals:

v/h/n (v->h->n)

d v/r/d (v->r->d

Note: There is no limit on number or sub paths or any level of nestedpaths are supported as shown in the following sample:v/{h/{r/d}{n}}{r/d}The above sample results:

v/h/r/d

v/h/n

v/r/d

Look for Changes in Configuration:

<resource type>[˜<attr id>, <attr id> , , , ]Ex: v/h[˜attr1,attr2]/nIt takes all resources of type ‘v’, finds the related resources of type‘h’ which have configuration attributes attr1 and atttr2 have changes inthe given time window. Then it finds resources of type ‘n’ which arerelated to the resulting resources of type ‘h’.

Find Patterns in Performance Data:

<resource type>[@*<attr id> <rx bld> IrxId <expression or id>][@ . . . ]<resource type>[@*#tw1#<attr Id> rx bid <expr . . . >]/<r type>[@̂tw1̂<attr id> <rx bl d> . . . ]<resource type>[@*#tw1#<attr Id> rx bld <expr . . . >]/<rtype>[@#tw2#̂tw1̂ <attr id> <rx bld> . . . ]

Where

*: ignores the resulted data_(—) 1) can be used to derive time windowsfor subsequent use_(—) 2) can be used to build logical pattern_b: for banded data d: for delta valuesSpecial note: Any numeric value in actual regex (exclusion=>quantifiers)should be prefix with “U” e.g [40-90]{5} will become [U40-U90]{5}. Herenumbers within the character class have been modified but not thequantifier i.e {5}.

Examples of Regular Expression Queries of Various Types EXAMPLES

-   -   v[@attr1 rx b U90₊]/h        It finds all the virtual machines which have performance data of        metric attr1 value equal or exceeds 90 in the given time window.        Then it finds the respective hosts. It also returns the matched        performance data    -   v[≠attr1 rxld rxp1]/h        It is similar to the example 2 but it specifies the regex        pattern id which will be defined in a separate file.    -   v[@#tw1# attr1 rx b U90₊]/h[@̂tw1̂attr12 rx b U80₊]        The first metric has defined a time span Id (tw1) which can be        referred by any other metric in the subsequent path. If metric        attr1 has generated any matched data and the respective time        windows will be assigned the id “tw1” and the same time windows        will be used on metric attr2. Note that if the connected host        has narrow time windows than the resulted tw1, the common slots        will be used on metric attr2.

Event Filter:

Syntax: [$*t:<regex pattern>,d:<regex pattern>]

Where

*: ignores the resulted data (won't produce any output but can be usedto build logical patterns)_ t: will search against the type of theevent_ d: will search against the description of the eventThe following are valid:

• [$t:rmAdded] // type check • [$d:error] // description check •[$t:rmAdded,d:error] // logical OR • [$*t:rmAdded] // type check andignore the result • [$*d:error] // description check and ignore theresult • [$*t:rmAdded,d:error] // local OR and ignore the result

Resource Addition/Deletion:

<resource type>[+<related resource types added> , , , ][−<relatedresource types removed> , , , ]Ex: v[+h,d,n][−h,d]The above expression will return resources of type ‘v’ on which relationof type ‘h’, ‘d’, ‘n’ has added or relation of type ‘h’, ‘d’ has beenremoved. How to exclude the data of a matched relation:<resource type>/*<related resource>/<sub resource>Ex: v/*h/dThe above express will return resources of type ‘v’ and the relatedresources of type ‘d’ directly. But, it will skip the data of thematched resources of type ‘h’ in the output.Note: One can mix any of the above combinations. One can specifyconfiguration changes, performance data filters, events list, multiplepaths, etc. in the same query.

Logical AND Operator

Logical AND operations are supported at path level and filter level.

At Path Level: _Syntax: P1/[&]P2/[&]P3/P4 . . .

Example 1: p1/&p2 _p1 && p2_Note: p1 qualifies only if p2 qualifiesExample 2: p1/&p2/&p3 _p1 && p2 && p3_Note: p2 is dependent on p3 and p1is dependent on p2Example 3: p1/p2/&p3 _p1, p2 && p3_Note: p1 can qualify irrespective ofp2 status but p2 can qualify only if p3 qualifiesExample 4: p1/&p2/p3/&p4 _p1&&p2, p3&&p4_Note: p2 can qualifyirrespective of p3 status

At Filter Level:

_Syntax: P1[filter1][&][filter 2][&][filter 3]/P2[filter 1][&][filter 2]

Example 1: p1[=1001 rx Demo3]&[@2001 rx b U10₊]_P1 qualifies if both thefilters find matches

Example 2: p[f1][f2]&[f3] (f1∥f2) && f3Example 3: p[f1]&[f2][&f3] f1 && f2 && f3Example 4: p[f1][f2][f3] f1∥f2∥f3Example 5: p[f1]&[f2][f3] f1 && (f2∥f3)

Note: if f1 fails, it exits (no processing of f2 or f3). Short circuitexecution on _Logical AND failure. But if f1 succeeds, it processes bothf2 and f3 irrespective of their results_Consider “∥” for union ratherthan logical OR.

Example 6: p[f1]&&&&&[f2] _f1 && f2_Note: multiple &s will be collapsedinto oneExample 7: p[f1][f2]& _f1∥f2_Note: trailing & will be ignored

Others

Regular expression patterns can include brackets, but only with matchingpairs.

When a resource is included in the higher level path, it will not berepeated in lower level paths.

Example

v[=attr1 rx Demo3]/*h/vIn third level in the result, Demo3 will not be repeated.*v[=attr1 rx Demo3]/*h/vSince in first level Demo3 is not included, it will appear in the thirdlevel

Regex Patterns

_Query supports both regular expression string or regular expressionpattern id which will be defined in a separate file in the followingformat:

<PatternList>_ <Pattern id=“ ”extraDataPoints=“”><![CDATA[<pattern>]]></Pattern>_</PatternList>_Example <PatternList>_ <Pattern id=“rxp1” extraDataPoints=“30”>_(—)<![CDATA[9+]]>_</Pattern>_(——)</PatternList>Pattern with id “rxp2” will directly apply the regular expressionpattern to the performance data.ExtraDataPoints will be used in the result set to return additional datain addition to the matched values. It adds 30 points before and after tothe matched values.

Query Processing Flow

The configuration data tells the system what types of resources haveperformance metric data stored in the system and what are the attributesof each type of resource, some of said attributes which may have hadperformance data measured. The configuration data basically tells whatresources have existed for what periods of time.

FIG. 7 is a flowchart of the processing of the query processor. When thequery processor starts, it first reads the query to determine the startand end times of the interval of performance data to be searched, andthen reads a configuration data file to determine for the time frame ofthe query (as set by the user by setting the start date and end date forthe query expression) what resources exist or have existed. Theseprocesses are represented by step 210. If a resource or resourcesexisted for only part of the relevant query interval, the queryprocessor determines from the configuration data the valid times theseresources existed during the relevant interval, and, if the resourcesstill exist, at what time they came into existence during the relevantquery interval. Resources can come and go such as when a server is takenoffline or a disk is swapped out. Reading the query and theconfiguration data file and determining what resources existed at anytime during the relevant interval is symbolized by step 210. Theconfiguration file also contains data which tells which resources arerelated to the resources named in the query. For example, a disk whichis contained in or connected to a particular server is indicated asrelated to that server.

The server reads all this data in the configuration file and, in step212, creates a map of only the relevant resources, i.e., the resourcesof the system that match the resource type identified at 208 in thequery of FIG. 6 and which existed at any time during the query intervaland any related resources. In the preferred embodiment, the string at208 identifies only a resource type. In this example of FIG. 6, theresource type is a host. Step 214 represents the process of loading theentire day of performance metric data for the relevant day, relevantresources (named resource and related resources) and the relevantattribute (the attribute named in the query). This results in all theperformance data for all resources of that type being loaded into memoryas described below for the entire day or days which include the relevantinterval starting at the start time and ending at the end timeidentified in query. These start and end times are given by the user inseparate boxes (not shown) from the query expression box when the userenters the query expression of FIG. 6 by interacting with a display on acomputer that shows the query box and start and end time boxes.

This filtering out of performance data for resources not of the namedtype allows the query processor to easily and quickly find performancemetric data which has been stored in the NDRB for only the relevantresource types indicated at 208 in the query syntax of FIG. 6.

The query processor then starts parsing the query expression anddetermines from element 213 of the query of FIG. 6 what type ofattribute data for the resource type named at 208 which is stored in theNDRB and which the query processor needs to perform the query. In theexample of the query of FIG. 6, parsing the query and reading portion213 thereof, the query processor determines it will be performing asearch on performance metric data for CPU usage on all hosts asidentfied by the string at 208. This is symbolized by step 214 of FIG.7.

Also in step 214, the query processor examines the start time (date andtime) and end time (date and time) set by the user on the query screen(not shown). The query processor then goes to the NDRB and examines thedirectory structures and finds the directory structures for the relevantday or days that contain the start time and end time of the query. Thequery processor then determines which subdirectory or subdirectories inthese relevant directories which contain performance metric data forresources of the type indicated at 208 in FIG. 6. The query processorthen determines the text files in the relevant subdirectories anddetermines which text files contain the performance metric data for thegroup of attributes which contain the attribute identified in the queryexpression, i.e., the attribute identified at 213. The query processoralso determines from the configuration data file what other resourcesare related to the resource types identified at 208 and loads theperformance metric data for these related resources for the relevantinterval into memory also, which is also part of step 214 in someembodiments.

Next, in step 216, the query processor determines whether the neededdata is already stored in cache. If so, the needed data is loaded fromthe cache memory to save the time of a disk read. If the needed data isnot stored in the cache, the query processor sends a read request to theoperating system API to read the appropriate text file or filescontaining the data needed for the query into memory in step 218. Step218 loads the entire day's worth of performance data for the resourcesof the type identified in the string at 208 in FIG. 6 and for the groupof attributes including the attribute identified at 213 of the queryexpression.

Now all the performance metric data for the file containing theperformance metric data for the entire group of attributes that containthe relevant attribute, and for the entire day or days spanning thestart date and end date are stored in memory. The data in memorycontains both performance metric data for attributes not named in thequery as well as performance metric data for the relevant attributewhich is outside the start time and end time given in the query. Toeliminate this excess data, the query process builds a new stringcontaining only the data for the relevant attribute and only starting atthe starting time and ending at the ending time named in the query. Thisprocess is symbolized by step 220. To do this, the query processor findsthe row in the loaded file which contains the performance metric datafor the relevant attribute identified at 213 of the relevant resourceidentified at 208 and counts entries until it reaches the value recordedfor the named start time. That performance metric value and allsubsequent values extending out to the end time are copied to a new filein the same sequence they were stored in the NDRB, all as symbolized bystep 220.

In step 222, the logic of the regular expression shown at 221 is appliedto the performance data in the new file created in step 220 to findvalues which meet the criteria expressed in the regular expression at221 of the search query for every resource of the type identified atstep 208. The values so found are returned and decoded from unicode backto the original performance metric values received from the probe. Ifmultiple substrings from multiple resources of the type indicated at 208are found which match the query, all such matching substrings arereturned along with identifying data as to which resource returned eachstring. In some embodiments including the preferred embodiment, themetadata about the resource identity (the specific host identity in theexample of FIG. 6), the attribute identity (CPU usage in the example ofFIG. 6), as well as the start time and end time of the query and thetimes the returned values were recorded is also returned for help inanalyzing the results. In some embodiments, only a true or false resultis returned. In some embodiments, if a true result is returned, and thesub string of performance metric values which matched the regularexpression is also returned after being decoded from unicode back to theperformance metric value received from the probe.

Nested Queries

Sometimes complex situations arise where trouble shooting of theperformance metric data is needed to solve a problem. An example wouldbe where a host is running multiple virtual machines and one of them hasslowed down considerably or stopped responding and the reason why needsto be determined. In such a case, a set of nested queries such as thosegiven below can be used to determine the source of the problem.

vm[@readlatency rx b [U20-U1000] {5}/h[@readlatency rx b[U20-U1000]{5}/vm[@readiop rx b [U1000-U2000]{5}]

The above query is actually three nested queries designed to drill downinto the performance data to find out what the problem is with a slowvirtual machine.

The first part of the query is: vm[@readlatency rx b [U20-U1000]{5}/This query looks at the readlatency attribute (a measure of speed)of all virtual machines which is between U20 and U1000 for 5 consecutivereadings. This range U20-U1000 finds all the virtual machines which arerunning pretty slow.

The question then becomes why are these virtual machines running slowly.To find that out, one question would be are the hosts that are executingthe code of the virtual machines themselves running slowly for somereason. In parsing this query, the query processor determines all hosttype resources which are related to the virtual machine type identifiedby the string vm at the beginning of the query. The performance metricdata for all these hosts is loaded into memory when the virtual machineperformance metric data is loaded into memory according to theprocessing of FIG. 7. In order to find out if the host or hosts arerunning slowly, the second part of the query is used. That part is:

h[@ readlatency rx b [U20-U1000]{5}/

This second part of the query looks at all the readlatency performancemetric values for host type resources that are related to the virtualmachine resource type identified in the first part of the query anddetermines which ones of these hosts are running slowly. The returneddata indicates which hosts have slow read latency. The question thenbecomes why is this host or hosts running slowly. To answer that, thethird part of the query is used. That part determines which virtualmachines which are related to the hosts have high IO operations going onwhich are bogging down the hosts. The third part of the query is:

vm[@readiop rx b [V1000-V2000]{5}]

This query returns the identities of the virtual machine which have highlevels of input/output operations going on. This high level of I/Ooperation will bog down the hardware of the host and will be theexplanation why other virtual machines have slowed down or stopped. Theresults can then be used to shut down the virtual machine that isbogging down the system or modify its operations somehow so as to notslow down the other virtual machines.

The results returned, for example, might indicate that virtual machine 1on host 1 is running slowly and host 1 is running slowly because virtualmachine 3 on that host is running a high number of I/O operations.Another set of data that matches the three queries may show also thatvirtual machine 2 running on host 2 is running slowly because host 2 isrunning slowly because virtual machine 4 running on host 2 is carryingout a high number of I/O operations.

Module Processing Flows

FIG. 8, comprised of FIGS. 8A through 8C, is a flowchart of theprocessing of the probe data importer. The Probe Data Importer runs aData Import Scheduler routine which runs data import operations atregular intervals, as symbolized by step 230. Step 232 checks the probedata folder for new data to be processed. Test 234 determines if newdata has arrived, and, if not, processing returns to step 230. If newdata has arrived, step 236 is performed to parse the list of files toget the list of configuration and performance metric data files in thenew data in sorted order. Test 238 determines if the new data hasperformance metric data in it. If so, step 240 is performed to importthe performance data. If the new data does not have performance datafiles in it, processing skips from step 238 to step 242 where a test isperformed to determine if configuration data has arrived. If not,processing returns to step 230 to wait for the next data import. If newconfiguration data has arrived, step 244 is performed to import the newconfiguration data.

Step 246 starts the processing of performance metric data files listedin the sorted list. Related performance counters of each resource willbe grouped together for storage and access optimization. Step 248creates file groups based on performance counter group wherein one filegroup is formed for each performance counter group. Step 250 creates athread pool and processes the file groups in multiple threads. UsingJava API (java.util.concurrent package), it creates a pool of threadsand each thread will pick one FileGroup at a time and processes it.After completion of one FileGroup processing, the same thread will pickthe next FileGroup, if any, for processing and the process repeats untilall the FileGroups are processed. Total thread count in the thread poolis configured through application properties file. Step 252 is theprocessing for each thread. In each thread, the files are read and theresources identified in the files are found and resource counter groupsare created. There is one resource counter group per each resource. Instep 254, another thread pool is formed, and the resource counter groupsare processed as explained above. In step 256, for each thread, theresource counter group data is processed, and data structures in memoryare updated to reflect the collected performance metric data for eachresource. The resource counters are used to determine where in each textfile each performance metric data value is to be stored to properlyreflect the time at which it was gathered. Finally, in step 258, thedata structures created in memory, i.e., the text files created when theperformance metric values are converted to unicode and stored in textfiles per the structure described elsewhere herein, are written to nonvolatile storage of the NRDB.

Step 260 on FIG. 8C represents the start of processing of theconfiguration files listed on the sorted list. In step 262, theconfiguration data file is parsed and the timestamp and resourcesignature is found. Test 264 determines whether the resource identifiedby the resource signature is found in the NRDB. If not, step 266 createsa minisnapshot file in the NRDB using the available configuration data.If test 264 determines that the resource identified in the configurationfile is already in the NRDB, step 268 is jumped to where theconfiguration changes and events are saved in an updates file in theNRDB. Finally, in step 270, the in-memory configuration data isrefreshed by re-loading it from the NRDB.

FIG. 9, comprised of FIGS. 9A and 9B, is a module diagram and flowchartof the processing of the NRDB Access manager module. The NRDB accessmanager module 300 controls access to the non relational data base filesystem 302 where the configuration data and performance metric data isstored. The NRDB access manager module 300 retrieves data from the NRDBand uses a cache 304 in memory of the server which is running module 300and a cache 306 in the file system to store data which is frequentlyaccessed to speed up data access. Performance data and configurationdata are imported from the probes by the Probe Data Importer module 308by the processing previously described and put into the NRDB via theNRDB access manage module 300. Query requests to analyze the performancemetric data in the NRDB are handled by Query Request Handler module 310which accesses the data in the NRDB via the NRDB Access Manager module300.

In FIG. 9B, the NRDB Access Manager processing starts with receiving arequest for performance metric data from the Query Process Handler, thisrequest symbolized by line 312. Step 314 determines if the requestedperformance data is in the performance data cache 304 in the system RAMand in the file system. If it is, step 316 is jumped to, and theperformance data is returned from the cache to the Query Process Handler310. If test 314 determines the performance data requested is not in thecache, step 318 is performed to load the requested data from the NRDBfile system into the cache 304, and then step 316 returns the requesteddata to the Query Process Handler 310.

The Probe Data Importer 308 adds updated and new configuration data andnew performance data via data path 321 to the NRDB through step 320, andupdates the respective configuration data cache 323 in RAM or theperformance data cache 304 in RAM and in the NRDB file system itself.NRDB Access Manager before processing performance metric data gets thein-memory representation (Java object) of the performance metric datathrough Performance cache. Performance cache first verifies in memorywhether it is already loaded from the file. If not, it loads the datafrom the file for the given date. If data is not available, it creates afile with template data (default values) for all the sampling intervalsfor that day. Based on the start time, it updates the in-memoryperformance metric data at appropriate locations. Once all the metricsdata in the group is processed, it commits the changes back to the file.The data will be compressed (deflate format) before saved into the file.

FIG. 10 is a block diagram of one embodiment of the overall systemincluding the major functional modules in the central server calledMegha™ where the query request processing for analysis of performancemetric data occurs and where the NDRB stores the performance metric dataand configuration data. Persons who want to query the performance metricdata send an asynchronous request using a web browser running on aclient computer 330 to a Web Request Controller 332 running on the Meghaserver using a REST application programmatic interface (API). The WebRequest Controller 332 receives the request, validates it and thenforwards it to the Query Request Processor module 310 with anasynchronous Java API call. Then the Web Request Controller returns thestatus to the client computer 330 by hinting that the client needs tocome back for the result. The Query Request Processor 310 processes therequest and incrementally saves the results in a Results Cache 311. Theclient computer 330 then sends back a request for the results to the WebRequest Controller 332 which checks the Results Cache 311. The resultsare then returned by the Web Request Controller 332 to the client 330 inan XML format if available. If the Query Request Processor is stillprocessing the request, the Web Request Controller send the status hintto the client indicating it needs to send another request for theresults later. The Report Engine 313 is a Java class object which sendsquery requests to the Query Request Processor 310 Java API invocationasynchronously and reads the results data from the Result Cache 311through a Java API.

FIG. 11 is a flowchart of the processing by one embodiment of the QueryRequest Processor. Step 320 parses the search query. If the search queryhas an invalid format, the result cache is updated with an error andprocessing is terminated. Each query starts with a high level resourcetype. The Query Request Processor reads the resource type and respondsby making a request in step 322 for all the performance metric data inthe NRDB for all resources of the type specified in the query. Thatrequest is made through the NRDB Access Manager. In step 324, a threadpool is created to process the data from each resource of the typeidentified in the query. Each thread processes data from one of theresources of the type identified in the query. The number of threadscreated is configured in the application properties file.

In step 326, any filters specified in the query are applied. Filters canbe things like configuration attribute matches, events, performance datapatterns, etc. All the specified filters are applied in sequentialorder. For example, the following query

vm[=name rx exchangevm1][$t:Network adapter added][@usedCapacity rx bu40₊]

has one configuration attribute match filter, an event filter and oneperformance data pattern match filter specified.

After applying the filters, if a thread finds that a resource'sperformance metric data meets the criteria specified in the query intest 328, then test 330 is performed. If test 328 determines that theperformance metric data of a resource does not meet the criteria in aquery, step 331 is performed to discard the performance metric data. Instep 330, the query is examined to determine if there is a sub path to asub resource specified therein. If there is a sub path specified, theperformance metric data of the sub path sub resource is loaded from theNRDB. Then any specified filters are applied again in step 326 todetermine if the sub resource qualifies, i.e., the performance metricdata of the sub resource meets the specified criteria in the query. Thisprocess continues until all sub paths specified in the query to subresources have been processed. When there are no more sub paths, or, ifthere were no sub paths specified in the first place, test 332 isperformed to determine if the top level resource qualified, and, if not,the data is discarded in step 331. If the top level resource doesqualify, the resource that qualified along with any performance datathat met the criteria specified in the query are added to the ResultCache in step 334.

Those skilled in the art will appreciate alternative embodiments that donot depart from the spirit and scope of the above described embodiments.All such alternative embodiments are intended to be included within thescope of the claims appended hereto.

What is claimed is:
 1. A process comprising: receiving a search queryfrom a user specifying search criteria using a regular expression havingpredefined syntax building blocks from which said regular expression canbe constructed, said query and regular expression to be used insearching performance metric data on a resource type named in said queryand on an attribute named in said query and having a start time and endtime specified by said user with said query; and accessing time-seriesperformance metric data which has been unicoded for the resource typeand attribute named in said query and for an interval the start and endof which is specified in said start time and end time of said query, andapplying said regular expression to said time-series performance metricdata; returning any metric data matching said search criteria expressedin said regular expression after conversion back from said unicode tothe numerical value in which it was received from a probe.
 2. Theprocess of claim 1 further comprising the step of returning data withsaid data matching said search criteria which identifies the resourcefrom which said data matching said search criteria was gathered and thetimes of the performance metric data values were recorded.
 3. Theprocess of claim 1 further comprising displaying on the display of acomputer which processes said query a search/query window in which auser enters the query expression including the regular expression aswell as a start time and an end time in which a user specifies the dataand time of interest for a start time indicating the start time forperformance metric data which is of interest, and an end time date andtime indicating the end time for the performance metric data which is ofinterest and which is to be searched using said query.
 4. The process ofclaim 1 further comprising the step of displaying predefined searchtemplates of various types which can be selected by a user and variablein said query and regular expression set by a user to customize thepredefined query to a particular situation of interest.
 5. The processof claim 1 wherein said time-series performance metric data which issearched is organized into a special data structure which has all theperformance metric data collected for all attributes of all resources inthe system being monitored stored in one directory dedicated to storingperformance metric data collected during only one day, the date of saidday being recorded in the name of said directory.
 6. The process ofclaim 5 wherein said directory has a subdirectory for every differenttype of resource in the system being monitored, and wherein eachsubdirectory stores files which store unicoded performance metric valuescollected on various attributes as text files.
 7. The process of claim 6wherein each text file stores therein unicoded performance metric valuesfor a group of attributes related to the performance of the resourcetype corresponding to said subdirectory.
 8. The process of claim 7wherein each text file has a separate section storing unicodedperformance metric values for just one attribute.
 9. The process ofclaim 8 wherein each said section stores the unicoded performance metricvalues for the attribute corresponding to said section as a commadelimited list.
 10. The process of claim 9 wherein said step ofaccessing time-series performance metric data comprises parsing saidquery to determine the start time, end time, resource type and attributeand accessing said special data structure and locating the directory ordirectories dedicated to storing performance metric data for the day ordays that encompass said start time and end time and determining thesubdirectory or subdirectories that contain text files storingperformance data for resources of the type indicated in said query andreading into memory of a computer the text files stored in saidsubdirectory or subdirectories which contain the attribute performancemetric data indicated in said query, and further comprising the step ofcreating a new string of performance metric data by accessing said textfile and copying all said performance metric data values for theattribute named in the query between said start time and said end timeinto a new file and only that data thereby eliminating all performancemetric data for attributes not named in said query and all performancemetric data which falls outside the interval bounded by said start timeand said end time.
 11. A process comprising: reading a query todetermine the start and end times of an interval of performance data tobe searched using the regular expression of said query; reading aconfiguration data file that defines the resources which existed at anytime during said interval and what resources are related to the resourceindicated in the query; creating a map of the relevant resources;determining from said query the performance metric data collected fromwhat resource type and what attribute of said resource type are to beread into memory; finding the relevant directory, subdirectories andtext files in a special data structure which stores performance metricdata values encoded as unicode hex values for all the resources andattributes of a system being monitored wherein said special datastructure stores each day's collected performance data in a separatedirectory as a time series of values from each attribute, eachattribute's measured values being stored as a time series in a sectionof a text file devoted to storing performance metric values for saidattribute, each said text file storing performance metric values for oneor more attributes in a group of attributes related to a particularresource type, each said text file dedicated to storing performancemetric values for one day for one particular resource of the type towhich a subdirectory in said directory dedicated to said day isincluded, said subdirectory dedicated to storing text files dedicatedonly to particular resources of the type to which said subdirectory isdedicated, said relevant directory, subdirectory and text files beingthe directory dedicated to the day that includes the interval bounded bythe start and end times, said relevant subdirectory being thesubdirectory dedicated to storing text files of attribute data in agroup of attribute which includes the attribute named in the query andthe relevant text files being one or more text files which include theattribute named in said query and which pertain to specific resources inthe group of resources identified in said query; reading the relevanttext files into memory of a computer; creating a new string ofperformance metric data by copying only the performance metric values insaid time series of performance metric values which are within theinterval bounded by said start time and end time of said query into anew file for each relevant text file read into memory; applying assearch criteria the criteria included in a regular expression includedwithin said query to said new file(s) and returning all values from alltext files which were searched which match said search criteria afterconversion back from said unicode values recorded in said text tiles tothe numeric values received from a probe from which said unicode valuesstored in said text file were created; and returning data whichidentifies which resource(s) generated said returned performance metricdata and when.
 12. The process of claim 11 further comprising thefollowing steps performed before step 1 of the process of claim 11:gathering performance metric data for one or more attributes of one ormore resources using one or more probe programs; organizing theperformance metric data collected into a data structure having: adirectory dedicated to storage of performance metric data collected forall attributes of all resource types on one calendar day; a separatesubdirectory for every type resource for which performance metric datawas collected; a separate text file in each subdirectory storing allperformance metric data for a group of attributes related to aparticular resource within the resource group to which said subdirectoryis devoted; and within each said text file, a separate section storingperformance metric data measurement values for each attribute in thegroup of attributes stored in said text file, said measurement valuesstored as a comma delimited list; compressing the data structure createdin the previous step; and transmitting said compressed data structureover any data path to another computer for analysis using the process ofclaim
 11. 13. The process of claim 12 further comprising the step ofcreating a first file in said data structure which comprises a list offiles in the data structure and including that list of files file as thefirst file in the collection of files which are compressed.
 14. Theprocess of claim 13 further comprising the step of collectingconfiguration data and creating a configuration file which is includedin the data structure which is compressed and transmitted.
 15. Theprocess of claim 15 further comprising the step of creating a snapshotfile as part of said configuration file, said snapshot file containingthe entire configuration data about the resource on which said probe isexecuting and collecting data upon, said snapshot file containing a sitename which is the location where said probe is being executed, a datasource which identifies the data source or resource about which saidprobe is collecting data, and the date and time the snapshot was createdincluding the beginning time and ending time for which the configurationdata is valid, the resource type and ID for each resource reported upon,the attribute ID, the resource type and any event ID and, the version ofthe file.
 16. The process of claim 15 further comprising the step ofcreating a minisnapshot file each time a configuration change orconfiguration related event occurs, said minisnapshot file having thesame format as said snapshot file.